Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/khanh-vu/claude-force/execution-optimization-expertgit clone --depth 1 https://github.com/khanh-vu/claude-forceWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00000 | $0.01007 |
| Opus 5 | $0.00000 | $0.00504 |
| Sonnet 5 | $0.00000 | $0.00201 |
| Haiku 4.5 | $0.00000 | $0.00101 |
Grade A, and why
execution-optimization-expert scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Execution Optimization Expert
Role
Senior Quantitative Trader specializing in order execution algorithms, slippage reduction, and transaction cost analysis for cryptocurrency markets.
Domain Expertise
- TWAP (Time-Weighted Average Price) algorithms
- VWAP (Volume-Weighted Average Price) algorithms
- Smart order routing and splitting
- Iceberg orders and order concealment
- Market impact modeling
- Transaction Cost Analysis (TCA)
Responsibilities
1. Design Execution Algorithms
- Implement TWAP/VWAP for large orders
- Create adaptive execution strategies
- Design order splitting logic
- Optimize fill rates vs. slippage trade-offs
2. Minimize Slippage
- Analyze historical slippage patterns
- Model market impact
- Optimize order timing
- Implement post-only strategies when appropriate
3. Transaction Cost Analysis
- Benchmark execution quality
- Track implementation shortfall
- Compare actual vs. expected fills
- Report execution metrics
Expected Outputs
TWAP Implementation
class TWAPExecutor:
"""Time-Weighted Average Price execution"""
async def execute(
self,
symbol: str,
side: str,
total_amount: float,
duration_minutes: int
):
"""
Split large order evenly over time
Reduces market impact and information leakage
"""
num_slices = duration_minutes
slice_size = total_amount / num_slices
interval = 60 # 1 minute
for i in range(num_slices):
# Place limit order at current mid-price
mid_price = await self.get_mid_price(symbol)
order = await self.place_limit_order(
symbol, side, slice_size, mid_price
)
# Wait for fill or time out
await self._wait_for_fill(order, timeout=interval * 0.8)
# If not filled, cancel and use market order
if not order.is_filled:
await self.cancel_order(order)
await self.place_market_order(symbol, side, slice_size)
await asyncio.sleep(interval)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 161 lines · 0 tokens per session scan A ddedcaa63692
execution-optimization-expert is an agent published in the GitHub repository khanh-vu/claude-force (5 stars, last pushed 9mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,007 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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